Introduction to Swarm intelligence
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Transcript of Introduction to Swarm intelligence
What for????• Capable for producing low cost• Fast• Robsut solution to several complex problem• Simple rule for each individual• Decentralized• Emerget
Inspired from the natural social behavior and dynamic movements with communication of birds , insects and fish.
Basic particle swarm optimization
In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality.
• Search area(possible solution)• Moving towards the promising area• Global best(each particles)• Each particles modifies its position according
to, current position current velocity distance between current position and pbest distance between current position and gbest
Algorithm parametersA:population of agentsPi:position of agents ai in the solution spacef :objective functionVi:velocity of agents ai
V(ai):neighbourhood of agents ai(fixed)
Algorithm[X*]=PSO()P=Particle_Intialization();for i=1 to it_maxFor each particle p in P dofp=f(p);If fp is better than f(p Best)pBest=p;endendgBest=best p in P;For each particle p in P doV=V+C1*rand*(pBest=p)+C2*rand*(gBest-p);P=p+V;endend
Particle Update Rulep=p+V with V=V+C1*rand*(pBest=p)+C2*rand*(gBest-
p);Where,p=particles positionV=path directionC1=weight of local information(personal best value)C2=weight of global information(neighbor best value)pBest=Best position of the particlegBest=Best position of the neighbor rand=Random variable
Update particle velocity
Vi ^t+1=Vi^t+C1V1^t(pbi^t=pi^t)+C2U2^t(gbi^t-pi^t)Diversification Intensification
Inertia Personal influence
Social influence
Makes particles moves in the same direction and
same velocity
Improves the individual makes the particle return from the current position
Makes the particle to follow the best solution
Advantages and Disadvantages
Few algorithm parametersEfficient global search algorithmSimple implementationEasily parallelized for concurrent programmingSlow convergence